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Data Science & Machine Learning

Navigating the Complexity of Life Science Research with Big Data as a Service

Trisha Dhawan
February 15, 2023

"In God we trust, all others must bring data"- William Edwards Deming

Big Data as a Service (BDaaS) has grown by leaps and bounds as unprecedented amounts of data get generated each year in all domains ranging from economics, engineering, and social media to medicine and drug discovery. The BDaaS market is expected to grow from USD 18.49 billion in 2021 to USD 124.02 billion by 2028; it is estimated to grow at a CAGR of 31.5% from 2022 to 2028.

The popular definition of Big Data, proposed by Gartner, uses the 3Vs: Volume, Velocity, and Variety. However, an additional dimension of Veracity to characterize Big Data is also commonplace. Big Data in life science research checks out all these characteristics.

As technology strengthens its hold on the life science research sector, the variety of data sources and volumes available for research and analysis are growing simultaneously. Large volumes of structured, unstructured, and semi-structured data collected from individual data elements and heterogeneous sources such as research labs, CROs, big pharma, and clinical trials provide insights into the causes and outcomes of diseases. The treasure trove of data contains crucial information and patterns required for better drug targets. Most importantly, the decision-makers are empowered with a data-driven approach to address pressing challenges.

With great volumes of data generated comes the great responsibility of wrangling, analyzing, and deriving insights for actionable outcomes. Leveraging the power of Big Data in the life science research domain has lagged owing to the reliance on standard regression-based methods with limitations. Recently, the life science research industry has caught wind and most organizations are jumping on the bandwagon of digital transformation in some shape or form.

BDaaS in Life Science Reasearch

BDaaS frees up valuable organizational resources by providing dedicated systems and software on the cloud or a contract for a managed system hosted and operated by a cloud vendor. Key cloud platform and infrastructure vendors like AWS, IBM, and Microsoft offer big data technology bundles and services to process, manage and analyze data for insight generation. Cloud-based advanced analytics encompasses applications like visualization, data mining, sentiment and semantic analysis, machine learning, statistics, network and cluster analysis, etc.

A shift in deployment from on-premises data centers that combined various open-source technologies to the cloud has occurred. This has resulted in a reduction in the complexity of the big data environments, easier scalability of systems, and increased flexibility to add and remove platforms, tools and technologies on the basis of organizational needs.


A few examples of how BDaaS can help sift through vast amounts of biomedical data faster:

  • Identifying genetic markers of diseases using advanced analytics and machine learning techniques including clustering analysis, classification algorithms, and association studies.
  • Analyzing clinical data for precision medicine by integrating data from heterogeneous sources, improving accessibility and findability, applying machine learning algorithms to find patterns, and developing predictive models.
  • Monitoring infectious disease spread using event detection, event characterization, enhanced surveillance, and formal epidemiologic investigation for data aggregation and management.

BDaaS has immense potential to simplify weighty, tedious tasks and processes in the biopharma R&D space, making room and time for more focus on what is even more important- analytics. The global life science analytics market is expected to grow at a CAGR of 11.8% from 2022 to 2027 and reach $47.5 billion by 2027!

Challenges in Adopting BDaaS

Although the BDaaS industry is expected to grow exponentially in the life science research space, there are a few challenges in the adoption of these services:

  1. High costs of implementation and subscription.
  2. A lack of understanding of the scientific and regulatory complexities of pharma by the software companies.
  3. Data governance becomes a challenge with the availability of unstructured, and semi-structured data types and formats making it difficult to integrate data from heterogeneous sources (public and proprietary).
  4. Switching from existing R&D processes is tedious in terms of time and skilled expertise, and billions of dollars are at stake.
  5. Data security is a significant concern when it comes to proprietary data and sensitive information.

By providing a scalable, efficient way to analyze vast amounts of data, BDaaS has the potential to revolutionize the way we approach health and disease. The 4Vs, as mentioned above, are as much a bane as a boon for the life science research industry and no single big data tool or software can handle such volumes and scale.

Elucidata’s Polly is a cloud-based platform that solves for much of these challenges and provides the largest volume of curated datasets ingested from various public sources at record speed!

Reach out to us to learn more about how you can integrate Polly into your existing workflows and pipelines to reach results faster!

This post was originally published in Polly Bits- our biweekly newsletter on LinkedIn.

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